Automatic Machine Translation for Bangla and English Resolving Ambiguities

Abstract

The strategic borderless knowledge sharing and development of communication interacts with dialects. Significant factors such as education, medical, business, research and others are vastly diffused over the world based on various lingoes. Bilingual or multilingual expression is the standard of having unknown/new linguistic along with its resources. The initial endeavor of the study is to implement the MT (machine translation) approaches for English to Bangla language processing and vice-versa. The emphasis of the study is the distinct ambiguities are identified along with their best solutions. Certain machine translation approaches such as word-to-word, direct, transfer, interlingua, corpus-based and statistical translation are surviving and, few of them are deployed in this Smart Natural Language Processing (SNLP) for dispatching the source to the target language and vice-versa. Two different dictionaries (bilingual and monolingual) are developed for the execution process. An eminent resource Stanford POS Tagger (as a toolkit) is used for identifying the grammatical structure of the source (English) dialect. This research also focuses on output acquiring through performance analyzing of different translation models.